Cloud GPUs are quickly becoming mainstream for big data applications like Spark on Kubernetes. Big data companies looking for scalability, speed, cost, as well as the energy and rack-space footprint of big data systems have turned their attention and budgets to GPUs. Although the massively parallel computing power of GPUs significantly speeds up these data-intensive ML and AI workloads, costs can spiral out of control.
Join Pepperdata Field Engineer Alex Pierce for a webinar on gaining visibility into cloud GPU resource utilization at the application level and improving the performance of your GPU-accelerated big data applications.
Topics include:
- Why GPU-accelerated big data applications are going mainstream
- Getting visibility into GPU memory usage and waste
- Fine-tuning GPU usage through end-user recommendations
- Manage costs at a granular level: attributing usage and cost to specific end-users
- Monitoring and eliminating waste with GPU monitoring solutions